Optimized Path Planning in Obstacle-Cluttered Environments*

This paper presents a path planning method called Adaptive Momentum-Preserving Path Planning (AMP) for environments with complex obstacle configurations. The approach considers local data and a multi-criteria cost function to optimize paths that prioritize safety, efficiency, and momentum conservation. The results demonstrate superior performance when compared to Probabilistic Roadmap and Rapidly-exploring Random Trees, achieving smoother paths with reduced computational complexity. The findings highlight the potential of AMP for enhancing efficiency and safety in collaborative robotics tasks.